Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning

BACKGROUND: Deep learning (DL)-based denoising methods have shown promise for reducing radiation dose and/or acquisition time in pediatric PET imaging. However, conventional DL approaches typically require large and diverse training datasets to achieve generalizability. We propose a patch-based DL (PDL) approach that learns local structural representations from a single high-quality examination, with the goal of improving image quality and preserving quantitative accuracy of reduced-count pediatric whole-body PET while minimizing training burden. METHODS: This retrospective study included PET/CT datasets acquired on a GE Discovery MI Gen 2 scanner. A single pediatric examination with high structural conspicuity and low noise was used for training. Testing was performed using a NEMA phantom and 88 clinical pediatric examinations (age < 12 years) acquired with routine pediatric FDG dosing and a 90‑s‑per‑bed protocol. Clinical list‑mode data were truncated to 20 s per bed to simulate reduced‑count acquisitions; training and phantom datasets were downsampled to match this noise level. The PDL model was trained using paired full‑count and reduced‑count image patches and applied to denoise reduced‑count phantom and patient images. Phantom evaluations assessed spatial resolution, contrast recovery, and noise. Clinical evaluation included a blinded observer study and quantitative SUV analysis, followed by assessment of the noise-bias tradeoff. RESULTS: was observed in the liver, mediastinal blood pool, and lesions, with no statistically significant differences compared with standard‑count images. Compared with highly regularized reconstruction, PDL achieved greater noise reduction with reduced bias. CONCLUSIONS: PDL enhancement for pediatric whole-body PET imaging may enable a reduction in acquisition time to 20 s per bed or a reduction in administered activity to approximately 22% of current clinical practice, while maintaining spatial resolution, contrast, quantitative accuracy, and diagnostic image quality.